cyttic/heb-synth-1m

Hebrew Synthetic Handwriting Lines — 1M

One million synthetic Hebrew handwritten text-line images with transcripts, built for pre-training Handwriting Text Recognition (HTR / OCR) models such as TrOCR. Each line is rendered from a large corpus of modern Hebrew sentences using 57 handwriting fonts, with per-line style variation and one of four image distortions.

Contents

Total images	1,000,000
Train / Test	979,869 / 20,131
Unique sentences	268,034
Image height	64 px (variable width)
Format	RGB PNG, black ink on white

Each of the four distortion tracks has exactly 250,000 images:

distortion	Effect
clear	clean render, no image distortion
blur	Gaussian blur, radius 0.8–1.8 (soft focus / ink bleed)
noise	additive Gaussian noise, σ 12–35 (scan grain)
rotate	whole-line rotation ±1–4°, random direction (skewed scan)

Split policy — leak-free by sentence

The train/test split is done by sentence: all four distortion variants of a given sentence land in the same split. A naive per-line split would leak a sentence's clear copy into train and its blurred copy into test, inflating apparent accuracy. Test fraction ≈ 2 %.

Columns

column	type	description
image	Image	the line image (64 px height)
text	string	the ground-truth transcript
distortion	string	one of clear / blur / noise / rotate

Usage

from datasets import load_dataset

ds = load_dataset("cyttic/heb-synth-1m")           # train + test
print(ds["train"][0]["text"], ds["train"][0]["image"].size)

# e.g. only the clean track:
clear = ds["train"].filter(lambda r: r["distortion"] == "clear")

How it was generated

• Text. ~268k modern Hebrew sentences generated by an LLM (DictaLM 2.0 Instruct), seed-conditioned on mid/low-frequency Hebrew words from a Wikipedia word-frequency list, filtered to Hebrew-only and ≤ ~12 words, exact-deduplicated. This deliberately covers modern vocabulary that literary Hebrew corpora miss.
• Rendering. 57 handwriting fonts (two families). Each line uses a random font plus randomized style knobs — letter-shape jitter, letter spacing ("tightness"), up/down baseline wobble, and small per-letter rotation — so the same sentence never renders identically. Malformed font glyphs (blobs, stray dirt-dots, broken strokes) are auto-detected and repaired by synthesizing a clean glyph, so the corpus is clean across all fonts.
• Distortion. After rendering, one of the four distortions above is applied.
• Constraint. Lines are kept within the 2304 px width limit (at 64 px height) used by block-style ViT preprocessing.

Intended use & limitations

Intended for Stage-1 synthetic pre-training of Hebrew HTR models, followed by fine-tuning on real handwritten data. It is synthetic: fonts approximate but do not replace genuine human handwriting variability (pen pressure, real paper backgrounds, connected cursive). No paper-texture backgrounds are included — images are on clean white.

Citation / provenance

Synthetic dataset generated by the DiffPen-Hebrew glyph-synthesis pipeline. Fonts are Hebrew handwriting fonts; verify individual font licenses before commercial use.